Abstract
Soil calcium carbonate (CaCO3) content is an important soil property. The prediction of soil CaCO3 content is necessary for the sustainable management of soil fertility. In this work, we attempted to incorporate environmental variables directly and through regression models into the framework of Bayesian maximum entropy (BME) to predict CaCO3 content. Firstly, multiple linear regression (MLR) and geographically weighted regression (GWR) were used to establish a relationship between sampling data and environmental variables, including Digital Elevation Model, pH, temperature, rainfall, and fluvo-aquic soils. Prediction results of MLR and GWR served as soft data and were incorporated into the framework of BME to estimate the CaCO3 content. Secondly, soil samples and environmental variables were combined to generate probability distributions of CaCO3 at unsampled points. These probability distributions were used as soft data for the BME to predict the CaCO3 content. The results showed that the GWR method (r = 0.84, RMSE = 24.0 g kg−1) performed better than the MLR method (r = 0.73, RMSE = 30.1 g kg−1). The BME-GWR method outperformed the BME-EV and BME-MLR methods. The r values of BME-GWR, BME-EV, and BME-MLR methods were 0.87, 0.86, and 0.82, respectively, and the RMSEs of the three methods were 22.2, 23.9, and 25.2 g kg−1, respectively. The spatial distribution of CaCO3 content predicted by the above methods was similar and significantly higher in the southwest than in the northeast.
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Acknowledgements
The authors wish to thank National Key Research and Development Program (Grants No. 2016YFC0201700) and Youth Program of National Natural Science Foundation of China (Grants No. 41907194) for funding this investigation.
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Shan, M., Liang, S., Fu, H. et al. Spatial prediction of soil calcium carbonate content based on Bayesian maximum entropy using environmental variables. Nutr Cycl Agroecosyst 120, 17–30 (2021). https://doi.org/10.1007/s10705-021-10135-8
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DOI: https://doi.org/10.1007/s10705-021-10135-8